Implementation of PIN ( Probability of Informed trading) on A-Share daily public data
based on Yan Y, Zhang S. An improved estimation method and empirical properties of the probability of informed trading[J]. Journal of Banking & Finance, 2012, 36(2): 454-467.
By construction, the probability of informed trading (PIN) measures the proportion of trades that are likely to be motivated by private information. PIN is usually estimated with intraday data.
Due to the limitation of the data availability, we here use daily public available data to calculate A-share daily level probabiliry of informed trading.
Data is downloaded from Wind. Download of the data is implemented in WindDataGet_dailyBS.py.
The only available buy/sell data available in Wind is the buy/sell amount of different investor types. As such, investor types and their critiria are as following:
A_1 | A_2 | A_3 | A_4 |
---|---|---|---|
Trading Quantity exceed 1000000 CNY | Trading Quantity exceed 500000 CNY | Trading Quantity exceed 150000 CNY | Trading Quantity exceed 40000 CNY |
PIN measures the fraction of trades in a day taht arise from informed traders, is defined as
where
informed traders who know the new information submit orders at the daily arrival rate
Two PIN parameter estimation methods are included in the code, the first is the Easley, Hvidkjaer, and O’Hara (EHO, 2010) method as the following:
The other is using the following joint likelihood function in MLE to overcome floating-point exception, from Lin and Ke (LK, 2011)
Reference:
Easley, D., Hvidkjaer, S., O’Hara, M., 2010. Factoring information into returns. Journal of Financial and Quantitative Analysis 45, 293–309.
Yan Y, Zhang S. An improved estimation method and empirical properties of the probability of informed trading[J]. Journal of Banking & Finance, 2012, 36(2): 454-467.
Lin, H.W., Ke, W.C., 2011. A computing bias in estimating the probability of informed trading. Journal of Financial Markets 14, 625–640.